Model Calculating Unit and Control Unit for Calculating a Neural Layer of a Multilayer Perceptron Model

2. The model calculating unit as recited in claim 1, wherein the processor core includes the state machine, a memory to store the one or multiple input variables of the input variable vector, the weighting matrix, the offset values specified for each neuron, and the output variables for each neuron, and one or multiple computing operation blocks, the computing operating blocks including a MAC block, and an activation function calculation block.

3. The model calculating unit as recited in claim 1, wherein the processor core is developed in a surface area of an integrated chip.

4. The model calculating unit as recited in claim 1, wherein the processor core is configured to base the calculation of the output variable on the distance values that are a function of the respective distances of the respective neuron values of the neurons from the values of the input variable vector, with the weighting of the distance values by the respective length scales assigned to the respective ones of the neurons.

5. The model calculating unit as recited in claim 4, wherein the distance values are quadratic distance values.

6. The model calculating unit as recited in claim 4, wherein the distance values are squares of the respective distances.

7. The model calculating unit as recited in claim 1, wherein the functional blocks include the state machine, the state machine being hardwired such that the state machine is configured to select between the implementing of the multilayer perceptron model and the at least one alternative model based on the first received input variable.

8. The model calculating unit as recited in claim 7, wherein the state machine is hardwired such that the state machine is configured to, when the multilayer perceptron model is selected instead of the at least one alternative model, additionally select between the implementing of the different activation functions with the multilayer perceptron model based on the second received input variable.

9. The model calculating unit as recited in claim 8, wherein the at least one alternative model includes at least one of a Gaussian process model and a Radial Basis Function (RBS) model.

10. The model calculating unit as recited in claim 8, wherein the processor core is configured to base the calculation of the output variable on the distance values that are a function of the respective distances of the respective neuron values of the neurons from the values of the input variable vector, with the weighting of the distance values by the respective length scales assigned to the respective ones of the neurons.

11. The model calculating unit as recited in claim 7, wherein the at least one alternative model includes at least one of a Gaussian process model and a Radial Basis Function (RBS) model.

12. The model calculating unit as recited in claim 11, wherein the different activation functions, between which the state machine is configured to select, include a kink function, a sigmoid function, a hyperbolic tangent function, and a linear function.

13. The model calculating unit as recited in claim 7, wherein the processor core is configured to base the calculation of the output variable on the distance values that are a function of the respective distances of the respective neuron values of the neurons from the values of the input variable vector, with the weighting of the distance values by the respective length scales assigned to the respective ones of the neurons.

14. The model calculating unit as recited in claim 1, wherein the functional blocks include the state machine, the state machine being hardwired such that the state machine is configured to select between the implementing of the different activation functions with the multilayer perceptron model based on the second received input variable.

15. The model calculating unit as recited in claim 14, wherein the processor core is configured to base the calculation of the output variable on the distance values that are a function of the respective distances of the respective neuron values of the neurons from the values of the input variable vector, with the weighting of the distance values by the respective length scales assigned to the respective ones of the neurons.

17. The control unit as recited in claim 16, wherein the control unit is developed as an integrated circuit.

19. The control unit as recited in claim 18, wherein the control unit is configured to output the control variables using the multilayer perceptron model cyclically as a function of a crankshaft angle of the engine system.